Grid Data Mining for Outcome Prediction in Intensive Care Medicine [chapter]

Manuel Filipe Santos, Wesley Mathew, Carlos Filipe Portela
2011 Communications in Computer and Information Science  
This paper introduces a distributed data mining approach suited to grid computing environments based on a supervised learning classifier system. SCM and MVM methods for Distributed Data Mining (DDM) are explored and compared with the Centralized Data Mining (CDM) approach. Experimental tests were conducted with a real world data set from the intensive care medicine in order to predict the outcome of the patients. The results demonstrate that the performance of the DDM methods are better than the CDM method.
doi:10.1007/978-3-642-24352-3_26 fatcat:u5hde3lkfbewreay457cbdjmcy